Motion detection based on observing several pictures

ABSTRACT

A method for motion detection based on observing several pictures is disclosed. Step (A) may compute a first motion score of an area in a target picture by a comparison of the area between the target picture and a first reference picture. Step (B) may compute a second motion score of the area by another comparison of the area between the target picture or a second reference picture and a third reference picture. Step (C) may temporal filter the target picture with the first reference picture based on the first motion score and the second motion score. At least one of the computing of the first motion score, the computing of the second motion score, and the temporal filtering may be controlled by one or more gain settings in a circuit. At least two of the first, the second, and the third reference pictures may be different pictures.

This application relates to U.S. Provisional Application No. 62/097,663,filed Dec. 30, 2014, which is hereby incorporated by reference in itsentirety.

FIELD OF THE INVENTION

The present invention relates to motion detection for video temporalfiltering generally and, more particularly, to methods and/or apparatusfor motion detection based on observing several pictures.

BACKGROUND OF THE INVENTION

Conventional motion detection looks at a local error measure, commonly asum-of-absolute-differences, between a target picture and a referencepicture. Even if no motion exists, such local error measures tend to benon-zero due to noise and changes in scene lightness. Therefore, motiondetection commonly detects small differences between the pictures as nomotion and detects big differences as motion. Temporal filtering is usedto combine a target picture with a motion compensated reference picture,and uses strong filtering where no motion is detected.

It would be desirable to implement motion detection based on observingseveral pictures.

SUMMARY OF THE INVENTION

The present invention concerns a method for motion detection based onobserving several pictures. Step (A) may compute a first motion score ofan area in a target picture by a comparison of the area between thetarget picture and a first reference picture. Step (B) may compute asecond motion score of the area by another comparison of the areabetween the target picture or a second reference picture and a thirdreference picture. Step (C) may temporal filter the target picture withthe first reference picture based on the first motion score and thesecond motion score. At least one of the computing of the first motionscore, the computing of the second motion score, and the temporalfiltering may be controlled by one or more gain settings in a circuit.At least two of the first, the second, and the third reference picturesmay be different pictures.

The objects, features and advantages of the present invention includeproviding motion detection based on observing several pictures that may(i) use motion detection between two different pairs of pictures todetermine how to apply a temporal filter between a pair of the pictures,(ii) use motion detection between non-adjacent pictures to determine howto temporal filter between adjacent pictures, and/or (iii) detect motionbased on motion between several picture pairs.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will be apparent from the following detailed description andthe appended claims and drawings in which:

FIG. 1 is a block diagram of a camera system;

FIG. 2 is a graphical representation of several motion detections;

FIG. 3 is a flow diagram of a motion detection method in accordance witha preferred embodiment of the present invention;

FIG. 4 is another graphical representation of several motion detections;

FIG. 5 is a diagram of a score combination by lookup;

FIG. 6 is a diagram of a score combination using maximum andtwo-dimensional combining; and

FIG. 7 is a diagram of a blending curve.

DETAILED DESCRIPTION OF EMBODIMENTS

Motion detection may be used in many applications, such as securitycameras, and/or in many operations, such as motion compensated temporalfiltering (e.g., MCTF) a sequence of pictures (or images). For themotion compensated temporal filtering, a filter may adaptively combineone or more reference (or previous) pictures and a target (or current)picture of the sequence based on detected motion in the target picturerelative to the reference pictures. The filtering may also decidelocally how to combine the multiple pictures (e.g., fields and/orframes) to reduce noise while limiting filter-created artifacts.

Typically, the filter may favor a reference picture more the more thefilter determines that no motion exists in a local area relative to thereference picture. For such a filter, motion may mean motion in anabsolute sense, if motion exists. In various embodiments, the referencepictures may be pre-transformed per a motion model (e.g., a process usedto estimate motion between the pictures). The transformed (motioncompensated) reference pictures may be subsequently combined with thetarget picture. For a motion compensated temporal filtering case, motiongenerally means motion between the motion compensated reference picturesand the target picture. For a non-motion compensated temporal filteringcase, motion generally means motion between the non-compensatedreference pictures and the target picture.

Referring to FIG. 1, a block diagram of a camera system 100 is shownillustrating an example implementation of a camera/recorder system (orapparatus). In some embodiments, the camera system 100 may be a digitalvideo camera, a digital still camera or a hybrid digital video/stillcamera. In an example, the electronics of the camera system 100 may beimplemented as one or more integrated circuits. For example, anapplication specific integrated circuit (e.g., ASIC) or system-on-a-chip(e.g., SOC) may be used to implement a processing portion of the camerasystem 100. In various embodiments, the camera system 100 may comprise acamera chip (or circuit) 102, a lens assembly 104, an image sensor 106,an audio codec 108, dynamic random access memory (e.g., DRAM) 110,non-volatile memory (e.g., NAND flash memory, etc.) 112, one or moreserial interfaces 114, an interface 116 for connecting to or acting as auniversal serial bus (e.g., USB) host, an interface for connecting to aremovable media 118 (e.g., SD—secure digital media, SDXC—secure digitalextended capacity media, etc.), a wireless interface 120 forcommunicating with a portable user device, a microphone 122 forrecording audio, and a speaker 124 for playing audio. In someembodiments, the lens assembly 104 and the image sensor 106 may be partof a separate camera connected to the processing portion of the system100 (e.g., via a video cable, a high definition media interface (e.g.,HDMI) cable, a USB cable, an ethernet cable, or wireless link).

In various embodiments, the circuit 102 may comprise a number of modules(or circuits) including, but not limited to, a pulse width modulation(e.g., PWM) module, a real time clock and watchdog timer (RTC/WDT), adirect memory access (e.g., DMA) engine, a high-definition multimediainterface (e.g., HDMI), an LCD/TV/Parallel interface, a general purposeinput/output (e.g., GPIO) and an analog-to-digital converter (e.g., ADC)module, an infrared (e.g., IR) remote interface, a secure digital inputoutput (e.g., SDIO) interface module, a secure digital (e.g., SD) cardinterface, an audio inter-IC sound (e.g., I2S) interface, an imagesensor input interface, and a synchronous data communications interface(e.g., IDC SPI/SSI). The circuit 102 may also include an embeddedprocessor (e.g., ARM, etc.), an image digital signal processor (e.g.,DSP), and a video and/or audio DSP. In embodiments incorporating thelens assembly 104 and image sensor 106 in the system 100, the circuit102 may be configured (e.g., programmed) to control the lens assembly104 and receive image data from the sensor 106. The wireless interface120 may include support for wireless communication by one or morewireless protocols such as Bluetooth®, ZigBee®, Institute of Electricaland Electronics Engineering (e.g., IEEE) 802.11, IEEE 802.15, IEEE802.15.1, IEEE 802.15.2, IEEE 802.15.3, IEEE 802.15.4, IEEE 802.15.5,and/or IEEE 802.20. The circuit 102 may also include support forcommunicating using one or more of the universal serial bus protocols(e.g., USB 1.0, 2.0, 3.0, etc.). The circuit 102 may also be configuredto be powered via the USB connection. However, other communicationand/or power interfaces may be implemented accordingly to meet thedesign criteria of a particular implementation.

In various embodiments, programming code (e.g., executable instructionsfor controlling various processors of the circuit 102) implementing atemporal filter with noise-robust and/or slow-motion robust motiondetection may be stored in one or more of the memories 110 and 112. Whenexecuted by the circuit 102, the programming code generally causes thecircuit 102 to receive a sequence of pictures from the sensor 106,temporal filter based on measurements if an area is stationary forseveral pictures, temporal filtering based on motion detection on smalland big areas, temporal filter based on comparing down-sampled pictures,and/or temporal filtering of adjacent pictures based on motion detectionof non-adjacent pictures.

For noisy image sequences, the differences between pictures, even instationary areas, may be large since the noise in each picture isdifferent. Moreover, slow motion tends to add only small amounts tomotion scores. Therefore, conventional motion detection may fail tocorrectly detect slow motion and/or motion in noisy sequences ofpictures. False positives (e.g., detecting motion where none exists) mayresult in too-noisy output pictures. False negatives (e.g., notdetecting actual motion) may result in temporal artifacts. Variousembodiments of the present invention generally contain one or more ofthe following features that may be used individually or in combinationto make temporal filtering based on motion compensation more robust.

Motion detection may be based on observing if the video is stationary ormoving for several pictures (or frames or fields). Specifically, for thesame location, scores are generally used from multiple picturecomparisons. By incorporating extra data into the still or movingdecision, the detection may be more robust.

Temporal filtering of adjacent pictures may be based on motion detectionof non-adjacent pictures. Adjacent pictures may be combined with atemporal filtering because adjacent pictures are generally more similarto each other than non-adjacent pictures. For slow motion, non-adjacentpictures may exhibit greater motion and, therefore, may exhibit highermotion scores than adjacent pictures. Performing detection onnon-adjacent pictures (e.g., a target picture and a non-adjacentreference picture) may provide a more robust detection of slow motion,especially in the presence of noise.

Referring to FIG. 2, a graphical representation 140 of several motiondetections is shown. Consider a sequence of several frames N to N−3(e.g., reference numbers 142 to 148). A motion detection 150 generallydetects motion between a target frame N (142) and a reference frame N−1(144). Another motion detection may be used to seek earlier motion. Amotion detection 152 generally detects motion between the referenceframe N−1 (144) and a reference frame N−2 (146). Still another motiondetection 154 may detect motion between the reference frame N−2 (146)and a reference frame N−3 (148). Up to all of the detections 150, 152,and 154 may be used to filter (156) samples between an area in thetarget frame N (142) and the area the reference frame N−1 (144). Whilethe example uses two earlier detections (e.g., the detection 152 and thedetection 154), any number of detections greater than a single detectionmay be used. The area may range from a single pixel to many pixels(e.g., 4×4, 8×8, 16×16, 32×32, or 64×64 blocks of pixels).

Referring to FIG. 3, a flow diagram of a motion detection method 160 isshown in accordance with a preferred embodiment of the presentinvention. The method (or process) 160 may be performed by the circuit102. The method 160 generally comprises a step (or state) 162, a step(or state) 164, a step (or state) 166, a step (or state) 168, a step (orstate) 170, a decision step (or state) 172, a step (or state) 174, astep (or state) 176, a decision step (or state) 178, and a step (orstate) 180. The steps 162-180 may be implemented in hardware, software,firmware or any combination thereof in an apparatus (or circuit ordevice). The sequence of the steps is shown as a representative example.Other step orders may be implemented to meet the criteria of aparticular application.

In the step 162, the circuit 102 may motion compensate one or morereference pictures (e.g., the frames N−1, N−2, N−3, etc.). The circuit102 may compare an area of the target picture (e.g., the frame N) to aspatially co-located area of a reference picture A (e.g., the frame N−1)in the step 164 to generate a raw score A (e.g., a target motion score).In some embodiments, the reference picture A may not be temporallyadjacent to the target picture N (e.g., the reference picture A may bethe frame N−2). In other embodiments, the reference picture A may betemporally adjacent to the target picture N (e.g., the reference pictureA may be the frame N−1). In the step 166, the area of the referencepicture A may be compared with the spatially co-located area of anotherreference picture B (e.g., the frame N−2) to generate another raw scoreB (e.g., an additional motion score). The area of the reference pictureB may be compared in the step 168 to the spatially co-located area of areference picture C (e.g., the frame N−3) to generate a raw score C(e.g., another motion score). The circuit 102 may combine two or threeof the three raw scores A, B and/or C in the step 170 to generate acombined score. The decision step 172 generally determines if additionaldetections may be useful in one or more additional areas. If theadditional detections may be useful, the steps 164-170 may be repeated.

In the step 174, the circuit 102 may use the combined score and a gainvalue, applied by the circuits 102 and/or 106, to temporal filter atarget sample in the area of the target picture N with another referencepicture E. The reference picture E (e.g., frame N−1 or N+1) may betemporally adjacent to the target picture N. In the step 176, thefiltered target sample may be stored in one or more of the memories(e.g., the memory 110).

A check may be performed in the decision step 178 to determine if anymore target samples exist in the current target picture N. If moretarget samples have yet to be processed, the method 160 may move to thenext unprocessed target sample and return to the temporal filter process(e.g., the step 174). Once all of the target samples in the currenttarget picture N have been processed, the method 160 may continue in thestep 180 with the target samples in the next picture.

The gain settings in the camera system 100 may include an analog gainand/or a digital gain in the image sensor 106, and/or a digital gain inthe circuit 102. One or more of such settings may be considered in thetemporal filtering. Furthermore, offset settings, exposure settingsand/or aperture settings may also be considered in the temporalfiltering. The circuit 102 generally controls the lens assembly 104and/or the image sensor 106 for an automatic exposure operation. Changesin the automatic exposure may change the light levels in the image datareceived from the sensor 106. The gain settings affect the noise inpictures; therefore, any of the steps computing the various scores(e.g., the steps 164, 166 and/or 168), combining the scores (e.g., thestep 170), and/or using the scores for temporal filtering (e.g., thestep 174) may be controlled based on the gain settings, offset settings,exposure settings and/or aperture settings.

The scores computed in the steps 164, 166 and/or 168 may be any scorethat is generally higher when motion exists between pictures. The scoresmay include, but are not limited to, sum-of-absolute-differences andsum-of-squared-differences. The scores may further be modified based ontone (e.g., brightness and/or color) as described in co-pending U.S.patent application Ser. No. 14/580,867, filed Dec. 23, 2014, which ishereby incorporated by reference in its entirety.

The steps 164-168 generally show three picture comparisons. In general,more or fewer picture comparisons may be implemented to meet thecriteria of a particular application. The combining operations may uselookup tables and/or mathematical transformations to generate thecombined motion scores. The step 170 generally shows combining two ormore scores from different pictures. FIGS. 5 and 6 may illustrateembodiments of various combination operations. Other comparisons betweenthe target frame N (142) and the reference frames may be implemented.

Referring to FIG. 4, a graphical representation 190 of several motiondetections is shown. Consider a sequence of multiple frames N to N−4(e.g., reference numbers 142 to 149). As in the representation 140, themotion detection 150 generally detects motion between the target frame N(142) and the reference frame N−1 (144). The detected motion mayestablish (e.g., the step 164 in FIG. 3) the raw score A. Another motiondetection 192 may detect motion between the target frame N (142) and thereference frame N−2 (146) to calculate the raw score B. The motiondetection 192 may be a variation of the step 166. In variousembodiments, a motion detection 194 may detect motion between the targetframe N (142) and the reference frame N−4 (149) to calculate the rawscore C. The motion detection 194 may be a variation of the step 168. Insome embodiments, the motion detection 194 may be between the targetframe N (142) and the reference frame N−3 (148). In other embodiments,the motion detection 194 may be between two of the reference frames(e.g., between the reference frame N−3 and the reference frame N−4).

The step 170 may combine two or three of the raw scores A, B and/or C tocalculate the combined score. The circuit 102 may use the combined scoreand the gain value in the step 174 to temporal filter a target sample inthe area of the target picture N with the reference picture E. Thereference picture E (e.g., frame N−1 or N+1) may be temporally adjacentto the target picture N. In the step 176, the filtered target sample maybe stored in one or more of the memories (e.g., the memory 110).Thereafter, additional target samples and additional target pictures maybe filtered.

Referring to FIG. 5, a diagram of an example score combination 200 bylookup table is shown. A signal 202 may carry scores from two or moreframes to a multi-dimensional lookup table (e.g., LUT) 204. An entry (orvalue) stored in the LUT 204 at an index formed by the scores may bepresented from the LUT 204 as a combined score in a signal 206.

Referring to FIG. 6, a diagram of an example score combination circuit(or module) 220 using maximum selection and two-dimensional combining isshown. The scores that do not use the target frame N may be received viaa signal 222 by a maximum circuit (or module) 226. The scores that usethe target frame N may be received by the maximum circuit 226 and acombine circuit (or module) 230 via a signal 224. The circuit 226 isgenerally operational to select a maximum score (or value) among thereceived scores. The maximum score may be passed in a signal 228 to thecircuit 230. The circuit 230 is generally operational to perform atwo-dimensional lookup or mathematical operations on the scores receivedin the signals 224 and 228 to generate and present a combined score in asignal 232.

Various embodiments of the circuit 230 may implement a two-dimensional(e.g., a dimension for the signal 224 and another dimension for thesignal 228) lookup. Other embodiments of the circuit 230 generallyselect the highest score in the signal 228. Some embodiments of thecircuit 230 may transform the maximum score per formula 1 as follows:Combined_score=((Max_score−SUB)×MUL)  (1)Where a subtraction value SUB and a multiplication value MUL may becontrollable parameters, and where a value Max_score may be the maximumscore in the signal 228. Still other embodiments may transform themaximum score with the score in the signal 224 as follows:

If(Max_score<THR) Combined_score=0;

-   -   else {        -   A=(CUR−SUB)×MUL)        -   Combined_score=max(Min_score, A)        -   }            Where a threshold THR, a minimum score Min_score, the            subtraction value SUB and the multiplication value MUL may            be controllable parameters. A current value CUR may be the            score that uses the target picture N in the signal 224. The            temporal filtering may combine the target picture N and a            reference picture using a blending curve.

Referring to FIG. 7, a diagram 240 of an example blending curve 242 isshown. A strength of the temporal filtering (or blending) may be acontinuum for one or more filter strengths. The diagram 240 generallyillustrates a range of medium filter strengths and fixed filterstrengths. A degree of filtering may depend on the blending curve 242.

An example of blending is generally determined as follows:

T=target (current) sample;

R=reference (previous) sample;

D=detected motion score; and

Alpha (curve 242)=lookup of the value D.

A filtered result (sample) may be calculated by formula 2 as follows:Result=(Alpha×T)+((1−Alpha)×R)  (2)In the diagram 240, the X axis generally represents the detected motionvalue D (e.g., the combined motion score of the target frame N). For8-bit levels of detected motion, the X axis is generally labeled from 0to 255. The Y axis generally represents an alpha value and ranges from 0(zero) to 1 (unity). Other ranges of D and alpha may be implemented tomeet the criteria of a particular application. Other techniques fordetermining the value D may also be implemented, such as consideringseveral target samples simultaneously.

Small detected motion values D may be illustrated in the section 244.The section 244 generally results in a low value of alpha per theblending curve 242. Medium (or intermediate) detected motion values Dmay be illustrated in the section 246. The section 246 generally resultsin a range of values for alpha per the blending curve 242. Largedetected motion values of D may be illustrated in the section 248. Thesection 248 generally results in a high value of alpha per the blendingcurve 242.

Where slow or no motion is detected, the value D is small and in thesection 244. Therefore, the value alpha may be small (and optionally afixed value). Per formula 2, the small value alpha generally weights theblending to favor the reference sample, or in some cases (e.g.,alpha=0.5) averages the reference sample with the target sample. Suchblending may be considered a strong filtering. Where medium motion isdetected, the value D may be medium and in the section 246. Thus, thevalue alpha may be medium. Per formula 2, the medium value alphavariably weights the blending between the target sample and thereference sample, depending on the level of motion. Such blending may beconsidered a medium filtering. Where fast motion is detected, the valueD may be large and in the section 248. Therefore, the value alpha may belarge and weights the blending to favor the target sample. Such blendingis generally considered a weak filtering. Where the value alpha=1, nofiltering is accomplished and the target sample is unchanged.

In various embodiments, the blending curve 242 may be implemented as oneor more LUTs. For example, a single LUT (e.g., LUT 204) may store allpoints of the blending curve 242. The value D may be implemented as thecombined score value.

In other embodiments, different LUTs may store different blending curvesand/or different portions of one or more blending curves. Selection of aparticular LUT is generally based on the combined score value. Forexample, if the combined score is zero, an LUT number 0 may be utilized.If the combined score is greater than zero and less than a threshold T1,an LUT number 1 may be utilized. If the combined score is greater thanthe threshold T1 and less than a threshold T2, an LUT number 2 may beutilized. If the combined score is greater than the threshold T2, an LUTnumber 3 is generally utilized. Other numbers of LUTs may be implementedto meet the criteria of a particular application.

In some embodiments, the combined score may be a lookup table number.The number of LUTs may be clamped per formula 3 as follows to a maximumvalue to avoid having too many LUTs:Table=min(combined score,number of tables−1)  (3)

In various embodiments, the combined score may be used to scale thevalue D received by the curve 242 or the LUT 204. The scaling may beimplemented per formula 4 as follows:D_used=D_before_multiplication×combined score  (4)

In other embodiments, the combined score may be used to offset the valueD received by the curve 242 or the LUT 204. The offsetting may beimplemented per formula 5 as follows:D_used=D_before_offset+combined score  (5)

The functions and structures illustrated in the diagrams of FIGS. 1-7may be designed, modeled and simulated using one or more of aconventional general purpose processor, digital computer,microprocessor, microcontroller and/or similar computational machines,programmed according to the teachings of the present specification, aswill be apparent to those skilled in the relevant art(s). Appropriatesoftware, firmware, coding, routines, instructions, opcodes, microcode,and/or program modules may readily be prepared by skilled programmersbased on the teachings of the present disclosure, as will also beapparent to those skilled in the relevant art(s). The software isgenerally embodied in a medium or several media, for example anon-transitory storage media, and may be executed by one or more of theprocessors. As used herein, the term “simultaneously” is meant todescribe events that share some common time period but the term is notmeant to be limited to events that begin at the same point in time, endat the same point in time, or have the same duration.

While the invention has been particularly shown and described withreference to the preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade without departing from the scope of the invention.

The invention claimed is:
 1. A method for motion detection based onobserving several pictures, comprising the steps of: computing a firstmotion score of an area in a target picture in a sequence of pictures bya comparison of said area between said target picture and a firstreference picture in said sequence of pictures; computing a secondmotion score of said area by another comparison of said area between asecond reference picture in said sequence of pictures and a thirdreference picture in said sequence of pictures; determining a maximumscore among (i) said first motion score that is based on said targetpicture and (ii) said second motion score that is based on said secondreference picture and said third reference picture; combining said firstmotion score and said maximum score to generate a combined motion score;and temporal filtering said target picture with said first referencepicture based on said combined motion score, wherein (i) at least one ofsaid computing of said first motion score, said computing of said secondmotion score, and said temporal filtering is controlled by one or moregain settings applied to said sequence of pictures in a circuit, (ii) atleast two of said first reference picture, said second referencepicture, and said third reference picture are different pictures and(iii) said target picture is a different picture than said firstreference picture, said second reference picture and said thirdreference picture.
 2. The method according to claim 1, where (i) saidcomputing of said combined motion score is controlled by one or moreparameters, and (ii) at least one of said parameters depends on at leastone of said gain settings.
 3. The method according to claim 1, whereinsaid combined motion score is read from a lookup table indexed by saidfirst motion score and said maximum score.
 4. The method according toclaim 1, further comprising the step of: transforming said first motionscore based on said combined motion score prior to said temporalfiltering to generate a transformed motion score, wherein a strength ofsaid temporal filtering is based on said transformed motion score. 5.The method according to claim 4, wherein (i) said transforming comprisesa plurality of transformations, and (ii) said transformed motion scoreis determined by one of said transformations selected by said combinedmotion score.
 6. The method according to claim 4, wherein saidtransforming comprises a multiplication of said first motion score bysaid combined motion score.
 7. The method according to claim 4, whereinsaid transforming comprises an addition of said combined motion score tosaid first motion score.
 8. The method according to claim 1, furthercomprising the step of: motion compensating said third reference picturerelative to said second reference picture prior to said computing ofsaid second motion score.
 9. An apparatus comprising: an interfaceconfigured to receive a sequence of pictures; and a circuit configuredto (i) compute a first motion score of an area in a target picture insaid sequence of pictures by a comparison of said area between saidtarget picture and a first reference picture in said sequence ofpictures, (ii) compute a second motion score of said area by anothercomparison of said area between a second reference picture in saidsequence of pictures and a third reference picture in said sequence ofpictures, (iii) determine a maximum score among (a) said first motionscore that is based on said target picture and (b) said second motionscores that are based on said second reference picture and said thirdreference picture, (iv) combine said first motion score and said maximumscore to generate a combined motion score and (v) temporal filter saidtarget picture with said first reference picture based on said combinedmotion score, wherein (a) at least one of said computation of said firstmotion score, said computation of said second motion score, and saidtemporal filter is controlled by one or more gain settings applied tosaid sequence of pictures, (b) at least two of said first referencepicture, said second reference picture, and said third reference pictureare different pictures and (c) said target picture is a differentpicture than said first reference picture, said second reference pictureand said third reference picture.
 10. The apparatus according to claim9, where (i) said computation of said combined motion score iscontrolled by one or more parameters, and (ii) at least one of saidparameters depends on at least one of said gain settings.
 11. Theapparatus according to claim 9, wherein said combined motion score isread from a lookup table indexed by said first motion score and saidmaximum score.
 12. The apparatus according to claim 9, wherein (i) saidcircuit is further configured to perform a transform of said firstmotion score based on said combined motion score prior to said temporalfilter to generate a transformed motion score, and (ii) a strength ofsaid temporal filter is based on said transformed motion score.
 13. Theapparatus according to claim 12, wherein (i) said transform comprises aplurality of transformations, and (ii) said transformed motion score isdetermined by one of said transformations selected by said combinedmotion score.
 14. The apparatus according to claim 12, wherein saidtransform comprises a multiplication of said first motion score by saidcombined motion score.
 15. The apparatus according to claim 12, whereinsaid transform comprises an addition of said combined motion score tosaid first motion score.
 16. The apparatus according to claim 9, whereinsaid circuit is further configured to motion compensate said thirdreference picture relative to said second reference picture prior tosaid computation of said second motion score.